AbstractIn this paper, we analyze a discretized version of the dynamic programming algorithm for a parameterized family of infinite-horizon economic models, and derive error bounds for the approximate value and policy functions. If h is the mesh size of the discretization, then the approximation error for the value function is bounded by Mh2, and the approximation error for the policy function is bounded by Nh, where the constants M and N can be estimated from primitive data of the model
A general model of dynamic optimization, deterministic, in discrete time, and with infinite time hor...
We propose a novel methodology for evaluating the accuracy of numeri-cal solutions to dynamic econom...
Models for long-term planning often lead to infinite horizon stochastic programs that offer signific...
This paper suggests a method for determining rigorous upper bounds on approximation errors of numeri...
We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economi...
In this paper we get error bounds for fully discrete approximations of infinite horizon problems via...
This paper provides a general framework for the quantitative analysis of stochastic dynamic models. ...
Abstract Suboptimal solutions to infinite-horizon dynamic optimization problems with continuous stat...
This paper studies fitted value iteration for continuous state dynamic programming using nonexpansiv...
We consider linear programming (LP) problems in infinite dimensional spaces that are in general comp...
In this paper we propose a recursive equilibrium algorithm for the numerical simulation of nonoptima...
We show that the Value Function Iteration (VFI) algorithm has difficulties approximating models with...
A description and comparison of several algorithms for approximating the solution to a model in whic...
In this paper infinite horizon optimal control problems for nonlinear high-dimensional dynamical sys...
Optimal control theory has a long history and broad applications. Motivated by the goal of obtaining...
A general model of dynamic optimization, deterministic, in discrete time, and with infinite time hor...
We propose a novel methodology for evaluating the accuracy of numeri-cal solutions to dynamic econom...
Models for long-term planning often lead to infinite horizon stochastic programs that offer signific...
This paper suggests a method for determining rigorous upper bounds on approximation errors of numeri...
We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economi...
In this paper we get error bounds for fully discrete approximations of infinite horizon problems via...
This paper provides a general framework for the quantitative analysis of stochastic dynamic models. ...
Abstract Suboptimal solutions to infinite-horizon dynamic optimization problems with continuous stat...
This paper studies fitted value iteration for continuous state dynamic programming using nonexpansiv...
We consider linear programming (LP) problems in infinite dimensional spaces that are in general comp...
In this paper we propose a recursive equilibrium algorithm for the numerical simulation of nonoptima...
We show that the Value Function Iteration (VFI) algorithm has difficulties approximating models with...
A description and comparison of several algorithms for approximating the solution to a model in whic...
In this paper infinite horizon optimal control problems for nonlinear high-dimensional dynamical sys...
Optimal control theory has a long history and broad applications. Motivated by the goal of obtaining...
A general model of dynamic optimization, deterministic, in discrete time, and with infinite time hor...
We propose a novel methodology for evaluating the accuracy of numeri-cal solutions to dynamic econom...
Models for long-term planning often lead to infinite horizon stochastic programs that offer signific...